Research Engineering Manager, AI Agent Harness (Product R&D)

𝗔𝗯𝘂 𝗗𝗵𝗮𝗯𝗶 (𝗨𝗔𝗘) · 𝗙𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗧𝗲𝗮𝗺, 𝗥&𝗗 · 𝗘𝗠, 𝗟𝟱 · 𝗠𝗝𝗗𝟬𝟯.𝟭.𝟬Hands-on Research Engineering Manager to establish the R&D team that builds an 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗵𝗮𝗿𝗻𝗲𝘀𝘀 and evaluation regime for 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗶𝗻𝗴 𝘄𝗲𝗯 𝗯𝗿𝗼𝘄𝘀𝗲𝗿𝘀 with open-weight LLMs. Greenfield.THE TECHNICAL CHALLENGEWe build agents on open-weight LLMs that operate web applications through the browser. The engineering work is the harness around the model: the abstraction that lets a pipeline or another agent drive a browser agent from the terminal, and the evaluation regime across levels of abstraction, from single actions ("find the element matching this description") to whole tasks, run against simulated applications before a live one. Agents maintain the evaluation suite; humans define its schema. Progress is measured against benchmarked results. You lead the team that builds this with the ability to write code.KEY RESPONSIBILITIES

• Set the technical direction of the harness and the interfaces between its levels of abstraction
• Establish the evaluation regime and the fortnightly score, and hold the team to measured results
• Run the R&D team on a two-week rhythm: planning, review with working software, retrospective, written post-mortems for failed experiments and incidents
• Coach, level and hire the engineers; interface with product management on the backlog and with the platform team on what the harness exposesDESIRED QUALIFICATIONS
• Managed both a research-cadence team and a sprint-cadence team, and can articulate the interface between them
• Built an agent harness or an evaluation harness for computer-use systems, agents or reinforcement learning, with an evaluation culture recognised outside the team, and can say which abstraction was right and which was wrong
• Has taken apart the harnesses of coding agents (OpenHands, SWE-agent, Aider, DeepSeek Harness or comparable) and can explain how they work in plain terms
• Has fine-tuned an LLM, from data preparation to evaluation on a held-out setEXPECTED QUALIFICATIONS
• T-shaped: deep in one domain, with working breadth in a neighbouring one
• Structures a large, incompletely specified problem and drives it to a working result independently
• Managed a team building on LLMs or machine-learning models in production, with evaluation pipelines as a management requirement and a clear account of how quality was gated
• Turns research or prototype code into reference implementations others reuse, using AI coding tools daily and verifying their output——HOW WE WORK
• Product engineering: we own what we build and run it in production
• Small teams, two-week cycles, working software at every review
• AI coding tools are part of the standard workflowWHAT WE OFFER
• Founding-team scope with a direct line to the Product CTO
• A supported track to the UAE Golden Visa (ten-year residency) for AI and technology talent, and relocation support including the UAE AI Specialist visa
• MacBook Pro M5 Max with 64 GB RAM or more, gear, Nvidia B200s and a serious budget for AI tokens, credits and tooling
• Up to six weeks per year working remotely from anywherePROCESS
• Introductory call with our recruiter
• Technical conversation with the Product CTO
• Practical session; the format is agreed with you
• In exercises, AI tools are allowed and expected (no LeetCode)WHO WE ARENew product organisation backed by a semi-government in Abu Dhabi. International, ex-FAANG team. Completely greenfield, with a modern tech stack.——REQUIREMENTS TO BE CONSIDERED
• Clear written and spoken English; has reported technical progress to non-technical executives on a fixed cadence
• 6+ years in engineering, of which 2+ leading a team of engineers where work was accepted on measured results
• Still coding, able to review TypeScript and Python; own code operated in production at a product company or a startup
• Bachelor's degree in any field, or self-taught with a track record of open-source contributionsRELATED TECHNOLOGIES AND CONCEPTS
• Agent harnesses and frameworks: OpenHands, SWE-agent, Aider, DeepSeek Harness, Hermes Agent, LangGraph, smolagents
• Programmatic prompt and pipeline optimisation: DSPy, TextGrad, Ax
• Browser automation and computer use: Playwright, Chrome DevTools Protocol, Stagehand, Browser Use, Steel Browser
• Evaluation and observability: evaluation harnesses, LLM-as-judge, trajectory evaluation, Inspect, Langfuse, OpenTelemetry; benchmarks such as Online-Mind2Web, WebArena, OSWorld
• Models and serving: open-weight LLMs, vLLM, SGLang
• State and orchestration: state machines, event sourcing, XState, Trigger.dev, Temporal, Kubernetes, TypeScript, Python